Volltext-Downloads (blau) und Frontdoor-Views (grau)

HATNER: Nested Named Entitiy Recognition for German

  • This paper describes our classification and rule-based attempt at nested Named Entity Recognition for German. We explain how both approaches interact with each other and the resources we used to achieve our results. Finally, we evaluate the overall performance of our system which achieves an F-score of 52.65% on the development set and 52.11% on the final test set of the GermEval 2014 Shared Task.

Download full text files

Export metadata

Additional Services

Share in Twitter    Search Google Scholar    frontdoor_oas
Metadaten
Author:Yulia Bobkova, Andreas Scholz, Tetiana Teplynska, Desislava Zhekova
URN:https://nbn-resolving.org/urn:nbn:de:gbv:hil2-opus-3044
ISBN:978-3-934105-47-8
Document Type:Conference Proceeding
Language:English
Date of Publication (online):2014/11/25
Release Date:2014/11/25
Tag:NER; Named entity recognition
GND Keyword:Computerlinguistik
Source:Workshop Proceedings of the 12th KONVENS 2014
PPN:Link zum Katalog
Institutes:Fachbereich III / Informationswissenschaft und Sprachtechnologie
DDC classes:400 Sprache / 400 Sprache, Linguistik
Collections:KONVENS 2014 / Workshop Proceedings of the 12th KONVENS 2014
Licence (German):License LogoCreative Commons - Namensnennung 3.0